Improved Results on State Estimation for Uncertain Takagi-Sugeno Fuzzy Stochastic Neural Networks with Time-Varying Delays

被引：0
|
作者：
Li, Yajun [1 ]
Deng, Feiqi [2 ]
Li, Jingzhao [1 ]
机构：
[1] Shunde Polytech, Coll Elect & Informat Engn, Foshan 528300, Peoples R China
[2] South China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China
来源：
INTERNATIONAL JOURNAL OF NONLINEAR SCIENCES AND NUMERICAL SIMULATION | 2017年 / 18卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Takagi-Sugeno; stochastic neural networks; state estimation; exponentially stable; linear matrix inequalities; EXPONENTIAL STABILITY; DISCRETE; SYSTEMS;
D O I：
10.1515/ijnsns-2016-0125
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The delay-dependent state estimation problem for Takagi-Sugeno fuzzy stochastic neural networks with time-varying delays is considered in this paper. We aim to design state estimators to estimate the network states such that the dynamics of the estimation error systems are guaranteed to be exponentially stable in the mean square. Both fuzzy-rule-independent and the fuzzy-rule-dependent state estimators are designed. Delay-dependent sufficient conditions are presented to guarantee the existence of the desired state estimators for the fuzzy stochastic neural networks. Finally, two numerical examples demonstrate that the proposed approaches are effective.
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页码：495 / 505
页数：11
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